Saturday, December 15, 2018

Off Canvas Menu in Oracle VBCS/JET Cloud

These days I'm actively working with VBCS/JET Cloud product from Oracle. The more I work with VBCS the more I like it. VBCS follows similar declarative development concepts as Oracle ADF, this makes it easy to get up to speed with VBCS development. VBCS with declarative JavaScript development approach brings unique solution for JavaScript systems implementation for enterprise.

I will share sample with off canvas menu implementation for VBCS app. Sample is based on step by step guide shared by Shay Shmeltzer. I don't describe steps how to build off canvas in VBCS from scratch, you should watch Shay's video for the instructions.

Off canvas menu rendered in VBCS app:

You should check how to build multiple flows in VBCS app in my previous post - Flow Navigation Menu Control in Oracle VBCS. I have defined three flows in my sample, this means there will be three menu items:

To render menu in off canvas block, I'm using JET navigation list component:

Sample app code which can be imported into your VBCS instance is available on GitHub.

Tuesday, December 11, 2018

Date Format Handling in Oracle JET

Oracle JET comes with out of the box support for date converter, check more about it in cookbook - Date Converter. This makes it very handy to format dates in JavaScript. Here is date picker field example with yyyy-MM-dd format applied:

When button Process is pressed, I take date value from date picker and add one day - result is printed in the log. This is just to test simple date operation in JavaScript.

Date picker is defined by JET tag. Format is assigned through converter property:

Current date is displayed from observable variable. This variable is initialized from current date converted to local ISO. Converter is configured with pattern. In the JS method, where tomorrow date is calculated - make sure to convert from ISO local date:

Hope this simple example helps you to work with dates in Oracle JET application. Source code is available on my GitHub directory.

Thursday, December 6, 2018

API for Amazon SageMaker ML Sentiment Analysis

Assume you manage support department and want to automate some of the workload which comes from users requesting support through Twitter. Probably you already would be using chatbot to send back replies to users. Bu this is not enough - some of the support requests must be taken with special care and handled by humans. How to understand when tweet message should be escalated and when no? Machine Learning for Business book got an answer. I recommend to read this book, my today post is based on Chapter 4.

You can download source code for Chapter 4 from book website. Model is trained based on sample dataset from Kaggle - Customer Support on Twitter. Model is trained based on subset of available data, using around 500 000 Twitter messages. Book authors converted and prepared dataset to be suitable to feed into Amazon SageMaker (dataset can be downloaded together with the source code).

Model is trained in such way, that it doesn't check if tweet is simply positive or negative. Sentiment analysis is based on the fact if tweet should be escalated or not. It could be even positive tweet should be escalated.

I have followed instructions from the book and was able to train and host the model. I have created AWS Lambda function and API Gateway to be able to call model from the outside (this part is not described in the book, but you can check my previous post to get more info about it - Amazon SageMaker Model Endpoint Access from Oracle JET).

To test trained model, I took two random tweets addressed to Lufthansa account and passed them to predict function. I exposed model through AWS Lambda function and created API Gateway, this allows to initiate REST request from such tool as Postman. Response with __label__1 needs esacalation and __label__0 doesn't need. Second tweet is more direct and it refers immediate feedback, it was labeled for escalation by our model for sentiment analysis. First tweet is a bit abstract, for this tweet no escalation:

This is AWS Lambda function, it gets data from request, calls model endpoint and returns back prediction:

Let's have a quick look into training dataset. There are around 20% of tweets representing tweets marked for escalation. This shows - there is no need to have 50%/50% split in training dataset. In real life probably number of escalations is less than half of all requests, this realistic scenario is represented in the dataset:

ML model is built using Amazon SageMaker BlazingText algorithm:

Once ML model is built, we deploy it to the endpoint. Predict function is invoked through the endpoint:

Saturday, December 1, 2018

Machine Learning - Date Feature Transformation Explained

Machine Learning is all about data. The way how you transform and feed data into ML algorithm - greatly depends training success. I will give you an example based on date type data. I will be using scenario described in my previous post - Machine Learning - Getting Data Into Right Shape. This scenario is focused around invoice risk, ML trains to recognize when invoice payment is at risk.

One of the key attributes in invoice data are dates - invoice date, payment due date and payment date. ML algorithm expects number as training feature, it can't operate with literals or dates. This is when data transformation comes in - out of original data we need to prepare data which can be understood by ML.

How we can transform dates into numbers? One of the ways is to split date value into multiple columns with numbers describing original date (year, quarter, month, week, day of year, day of month, day of week). This might work? To be sure - we need to run training and validate training success.


1. Sample Jupyter notebooks and datasets are available on my GitHub repo
2. I would recommend to read this book - Machine Learning for Business

Two approaches:

1. Date feature transformation into multiple attributes

Example where date is split into multiple columns:

Correlation between decision column and features show many dependencies, but it doesn't pick up all columns for payment date feature. This is early sign training might not work well:

We need to create test (1/3 of remaining data), validation (2/3 of remaining data) and training (70% of all data) datasets to be able to train, validate and test ML model. Splitting original dataset into three parts:

Running training using XGBoost (Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes). Read more about XGBoost parameters. We have validation dataset and this allows to use XGBoost early stopping functionality, if training quality would not improve in N (10 in our case) rounds - it will stop and pick best iteration as the one to be used for training result:

Result: training accuracy 93% and validation accuracy 74%. Validation accuracy is too low, this means training wasn't successful and we should try to transform dates in another way:

2. Date feature transformation into difference between dates

Instead of splitting date into multiple attributes, we should reduce number of attributes to two. We can use date difference as such:

- Day difference between Payment Due Date and Invoice Date
- Day difference between Payment Date and Invoice Date

This should bring clear pattern, when there is payment delay - difference between payment date/invoice date will be bigger than between payment due date/invoice date. Sample data with date feature transformed into date difference:

Correlation is much better this time. Decision correlates well with date differences and total:

Test, validation and training data sets will be prepared in the same proportions as in previous test. But we will be using stratify option. This option helps to shuffle data and create test, validation and training data sets where decision attribute is well represented:

Training, validation and test datasets are prepared:

Using same XGBoost training parameters:

Result: This time we get 99% training accuracy and 97% validation accuracy. Great result. You can see how important is data preparation step for ML. It directly relates to ML training quality:

Wednesday, November 28, 2018

Notification Messages in Oracle JET

Let's take a look into cool component available in Oracle JET - notification messages (it is possible to display messages in different ways - inline or overlay. Check more about messages functionality in JET Cookbook example).

This is how notifications messages are showing up, very cool way to send information to the user:

Messages are implemented with oj-messages components. This component accepts observable array of messages to be displayed. We can specify how message is displayed (notification in this case), position information and close listener (where we can remove message info entry from messages array):

In sample processAction function I'm pushing new entries into messages array. To simulate delay for second message, pushing second entry with 1 second delay. Once message is closed after standard delay time - function closeMessageHandler is invoked, where we are removing entry from array:

Sample application code is available on my GitHub repo.

Monday, November 26, 2018

Our new product - Katana 18.1 (Machine Learning for Business Automation)

Big day. We announce our brand new product - Katana. Today is first release, which is called 18.1. While working with many enterprise customers we saw a need for a product which would help to integrate machine learning into business applications in more seamless and flexible way. Primary area for machine learning application in enterprise - business automation.

Katana offers and will continue to evolve in the following areas:

1. Collection of machine learning models tailored for business automation. This is the core part of Katana. Machine learning models can run on Cloud (AWS SageMaker, Google Cloud Machine Learning, Oracle Cloud, Azure) or on Docker container deployed On-Premise. Main focus is towards business automation with machine learning, including automation for business rules and processes. Goal is to reduce repetitive labor time and simplify complex, redundant business rules maintenance

2. API layer built to help to transform business data into the format which can be passed to machine learning model. This part provides API to simplify machine learning model usage in customer business applications

3. Monitoring UI designed to display various statistics related to machine learning model usage by customer business applications. UI which helps to transform business data to machine learning format is also implemented in this part

Katana architecture:

One of the business use cases, where we are using Katana - invoice payment risk calculation. UI which is calling Katana machine learning API to identify if invoice payment is at risk:

Currently we offer these machine learning models:

1. Invoice payment risk calculation

2. Automatic order approval processing

3. Sentiment analysis for user complaints

Get in touch for more information.

Sunday, November 25, 2018

Oracle ADF + Jasper Visualize.js = Awesome

This week I was working on a task to integrate Jasper Visualize.js into Oracle ADF application JSF page fragment. I must say integration was successful and Jasper report renders very well in Oracle ADF screen with the help of Visualize.js. Great thing about Visualize.js - it renders report in ADF page through client side HTML/JS, there is no iFrame. Report HTML structure is included into HTML generated by ADF, this allows to use CSS to control report size and make it responsive.

To prove integration, I was using ADF application with multiple regions - ADF Multi Task Flow Binding and Tab Order. Each region is loaded with ADF Faces tab:

One of the tabs display region with Jasper report, rendered with Visualize.js:

Check client side generated code. You should see HTML from Visualize.js inside ADF generated HTML structure:

It is straightforward to render Jasper report with Visualize.js in Oracle ADF. Add JS resource reference to Visualize.js library, define DIV where report supposed to be rendered. Add Visualize.js function to render report from certain path, etc.:

Sample code is available on my GitHub repo.